The focus of the proposed research is to understand the effect of sequence variation on the function of molecular networks. We will develop computational algorithms that integrate genotype, gene expression and phenotype data to construct models that describe how sequence variation perturbs the regulatory network, alters signal processing and is manifested in cellular phenotypes. Our approach is based on Bayesian networks, a framework we pioneered for the reconstruction of molecular networks from high-throughput data. We recently applied this framework to develop the Geronemo algorithm which we applied to yeast and uncovered a novel relationship between the sequence specific RNA factor PUF3 and P-Bodies, as well as a Single Nucleotide Polymorphism (SNP) in MKT1 that modulates this relationship. Both novel findings were experimentally validated subsequent to their discovery. Our approach is based on the complementary duality between genetic sequence and functional genomics. A significant influence of genotype on phenotype is induced by fine tuned perturbations to the complex regulatory network that governs a cell's activity. Variation in the expression of a single gene is more tractable and can be used as an intermediary to help associate genetic factors to the more complex downstream changes in phenotype in a hierarchical fashion. Conversely, DNA sequence polymorphisms are effective perturb-agens which provide a rich source of variation to help uncover regulatory relations in the molecular network as well as direct their causality. We will develop our methods using a large collection of highly variable yeast strains, for which we have generated robust quantitative growth curves under numerous environmental conditions. The methodologies piloted in yeast will be extended to genotype and gene expression data derived from tumor samples to attempt to elucidate the multiple genetic factors that drive their proliferation. These tools will be made publicly available, including a friendly graphical user interface and visualization.